Comparative Analysis of CNN Models for ALL Diagnosis

Comparative Analysis of CNN Models for ALL Diagnosis

Vinod A M
Department of CSE Malnad College of Engineering

Hassan, India

Satvik Hegde 4MC21CS135
Department of CSE

Sanjay B S 4MC21CS133
Department of CSE Malnad College of Engineering

Hassan, India

Varshini H V 4MC21CS165
Department of CSE

Malnad College of Engineering

Hassan, India

Sanjay Vasista G S 4MC21CS134
Department of CSE Malnad College of Engineering

Hassan, India

Malnad College of Engineering

Hassan, India

Abstract—Acute Lymphoblastic Leukemia (ALL) is a serious pediatric cancer where early and accurate diagnosis is vital for improving treatment outcomes. This project explores the use of Convolutional Neural Networks (CNNs) to develop an effective diagnostic system for identifying ALL from microscopic blood smear images. Leveraging the ALL IDB1 dataset, six pre-trained CNN models—VGG16, VGG19, ResNet101V2, DenseNet201, Mo- bileNetV2, and InceptionV3—were fine-tuned and assessed based on key performance metrics including accuracy, precision, recall, F1-score, and AUC-ROC. To improve model robustness and reduce overfitting, data augmentation techniques were applied. A comparative evaluation highlighted the strengths and weaknesses of each model, as well as their potential for real-world clinical use. The findings demonstrate the power of deep learning in medical image analysis, offering faster, more affordable, and accurate alternatives to traditional diagnostic methods. This research not only validates the role of CNNs in ALL detection but also moves closer to integrating AI-driven diagnostics into everyday clinical practice, enhancing the accessibility and reliability of healthcare delivery.